Respiratory impedance measured using impulse oscillometry in a healthy urban population

• Main Authors: Kenneth I. Berger, Margaret Wohlleber, Roberta M. Goldring, Joan Reibman, Mark R. Farfel, Stephen M. Friedman, Beno W. Oppenheimer, Steven D. Stellman, James E. Cone, Yongzhao Shao
• Format: Article
• Language: English
• Published: European Respiratory Society 2021-03-01
• Series: ERJ Open Research
• Online Access: http://openres.ersjournals.com/content/7/1/00560-2020.full

This study derives normative prediction equations for respiratory impedance in a healthy asymptomatic urban population using an impulse oscillation system (IOS). In addition, this study uses body mass index (BMI) in the equations to describe the effect of obesity on respiratory impedance. Data from an urban population comprising 472 healthy asymptomatic subjects that resided or worked in lower Manhattan, New York City were retrospectively analysed. This population was the control group from a previously completed case–control study of the health effects of exposure to World Trade Center dust. Since all subjects underwent spirometry and oscillometry, these previously collected data allowed a unique opportunity to derive normative prediction equations for oscillometry in an urban, lifetime non-smoking, asymptomatic population without underlying respiratory disease. Normative prediction equations for men and women were successfully developed for a broad range of respiratory oscillometry variables with narrow confidence bands. Models that used BMI as an independent predictor of oscillometry variables (in addition to age and height) demonstrated equivalent or better fit when compared with models that used weight. With increasing BMI, resistance and reactance increased compatible with lung and airway compression from mass loading. This study represents the largest cohort of healthy urban subjects assessed with an IOS device. Normative prediction equations were derived that should facilitate application of IOS in the clinical setting. In addition, the data suggest that modelling of lung function may be best performed using height and BMI as independent variables rather than the traditional approach of using height and weight.